The R language has extensive graphical capabilities.
Graphics in R may be created by many different methods including base graphics and more advanced plotting packages such as lattice.
The ggplot2 package was created by Hadley Wickham and provides a intuitive plotting system to rapidly generate publication quality graphics.
ggplot2 builds on the concept of the “Grammar of Graphics” (Wilkinson 2005, Bertin 1983) which describes a consistent syntax for the construction of a wide range of complex graphics by a concise description of their components.
The structured syntax and high level of abstraction used by ggplot2 should allow for the user to concentrate on the visualisations instead of creating the underlying code.
On top of this central philosophy ggplot2 has:
ggplot(data = <default data set>,
aes(x = <default x axis variable>,
y = <default y axis variable>,
... <other default aesthetic mappings>),
... <other plot defaults>) +
geom_<geom type>(aes(size = <size variable for this geom>,
... <other aesthetic mappings>),
data = <data for this point geom>,
stat = <statistic string or function>,
position = <position string or function>,
color = <"fixed color specification">,
<other arguments, possibly passed to the _stat_ function) +
scale_<aesthetic>_<type>(name = <"scale label">,
breaks = <where to put tick marks>,
labels = <labels for tick marks>,
... <other options for the scale>) +
ggtitle("Example Plot For intermediateR")+
xlab("Plotting packages")+
ylab("Productivity")+
theme(plot.title = element_text(colour = "gray"),
... <other theme elements>)
Earlier we have been working with the patient’s dataset to create a “clean and tidy” dataset.
Now we will use this dataset to demonstrate some of the plotting capabilities of ggplot2.
Now our data is clean and tidy.
library(tidyr)
library(ggplot2)
library(dplyr)
library(stringr)
library(lubridate)
patients_clean <- read.delim("patients_clean_ggplot2.txt",sep="\t")
knitr:::kable(head(patients_clean))
| ID | Name | Race | Age | Sex | Smokes | Height | Weight | Birth | State | Pet | Grade_Level | Died | Count | Date | Grade | BMI | Overweight |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Patient-001 | Demetrius | White | 44 | Male | Smoker | 176.4791 | 77.07070 | 1972-02-11 | New York | Cat | 1 | TRUE | -0.7440011 | 2015-03-04 | 1 | 24.74586 | FALSE |
| Patient-002 | Rosario | White | 43 | Male | Smoker | 174.7485 | 83.26835 | 1972-07-22 | Florida | Cat | 1 | TRUE | -1.0714531 | 2015-03-04 | 1 | 27.26799 | TRUE |
| Patient-003 | Julio | Black | 44 | Male | Non-Smoker | 173.9449 | 85.63231 | 1971-11-23 | Connecticut | Dog | 2 | FALSE | -0.8215636 | 2015-03-04 | 2 | 28.30182 | TRUE |
| Patient-004 | Lupe | White | 44 | Male | Non-Smoker | 180.0289 | 88.72032 | 1971-10-03 | Massachusetts | Dog | 1 | TRUE | 0.4976671 | 2015-03-04 | 1 | 27.37403 | TRUE |
| Patient-005 | Lavern | Cat | 43 | Male | Non-Smoker | 178.6365 | 97.05405 | 1972-11-23 | Kansas | Cat | 3 | FALSE | -1.2099217 | 2015-03-04 | 3 | 30.41397 | TRUE |
| Patient-006 | Bernie | Native | 43 | Female | Non-Smoker | 159.7778 | 68.31247 | 1972-07-30 | Illinois | Cat | 2 | FALSE | 0.5241618 | 2015-03-04 | 2 | 26.75882 | TRUE |
In order to produce a ggplot2 graph we need a minimum of:-
In the code below we define the data as our cleaned patients data frame.
pcPlot <- ggplot(data=patients_clean)
class(pcPlot)
## [1] "gg" "ggplot"
pcPlot$data[1:4,]
## ID Name Race Age Sex Smokes Height Weight
## 1 Patient-001 Demetrius White 44 Male Smoker 176.4791 77.07070
## 2 Patient-002 Rosario White 43 Male Smoker 174.7485 83.26835
## 3 Patient-003 Julio Black 44 Male Non-Smoker 173.9449 85.63231
## 4 Patient-004 Lupe White 44 Male Non-Smoker 180.0289 88.72032
## Birth State Pet Grade_Level Died Count Date
## 1 1972-02-11 New York Cat 1 TRUE -0.7440011 2015-03-04
## 2 1972-07-22 Florida Cat 1 TRUE -1.0714531 2015-03-04
## 3 1971-11-23 Connecticut Dog 2 FALSE -0.8215636 2015-03-04
## 4 1971-10-03 Massachusetts Dog 1 TRUE 0.4976671 2015-03-04
## Grade BMI Overweight
## 1 1 24.74586 FALSE
## 2 1 27.26799 TRUE
## 3 2 28.30182 TRUE
## 4 1 27.37403 TRUE
Now we can see that we have gg/ggplot object (pcPlot) and in this the data has been defined.
Important information on how to map the data to the visual properties (aesthetics) of the plot as well as what type of plot to use (geom).
pcPlot$mapping
## * NULL ->
pcPlot$theme
## list()
pcPlot$layers
## list()
The information to map the data to the plot can be added now using the aes() function.
pcPlot <- ggplot(data=patients_clean)
pcPlot <- pcPlot+aes(x=Height,Weight)
pcPlot$mapping
## $x
## Height
##
## $y
## Weight
pcPlot$theme
## list()
pcPlot$layers
## list()
But we are still missing the final component of our plot, the type of plot to use (geom).
Below the geom_point function is used to specify a point plot, a scatter plot of x values versus y values.
pcPlot <- ggplot(data=patients_clean)
pcPlot <- pcPlot+aes(x=Height,Weight)
pcPlot <- pcPlot+geom_point()
pcPlot$mapping
## $x
## Height
##
## $y
## Weight
pcPlot$theme
## list()
pcPlot$layers
## [[1]]
## geom_point: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity
Now we have all the components of our plot we need we can display the results.
pcPlot
More typically, the data and aesthetics are typically defined within ggplot function and geoms applied afterwards.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height,Weight))
pcPlot+geom_point()
As we have seen, an important element of a ggplot is the geom used. Following the specification of data, the geom describes the type of plot used.
Several geoms are available in ggplot2:-
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height,Weight))
pcPlot_line <- pcPlot+geom_line()
pcPlot_line
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height,Weight))
pcPlot_smooth <- pcPlot+geom_smooth()
pcPlot_smooth
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height))
pcPlot_bar <- pcPlot+geom_bar()
pcPlot_bar
# pcPlot <- ggplot(data=patients_clean,
# mapping=aes(x=Height,))
#
# pcPlot_bar <- pcPlot+geom_bar()
#
# pcPlot_bar
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height))
pcPlot_hist <- pcPlot+geom_histogram()
pcPlot_hist
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height))
pcPlot_density <- pcPlot+geom_density()
pcPlot_density
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Sex,y=Height))
pcPlot_boxplot <- pcPlot+geom_boxplot()
pcPlot_boxplot
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Sex,y=Height))
pcPlot_violin <- pcPlot+geom_violin()
pcPlot_violin
In order to change the property on an aesthetic in plot into a constant value (e.g. set colour of points to red), the we can supply the colour argument to the geom_point() function.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height,Weight))
pcPlot+geom_point(colour="red")
As we discussed earlier however, ggplot2 makes use of aesthetic mappings to assign variables in the data to the properties/aesthetics of the plot. This allows the properties of the plot to reflect variables in the data dynamically.
In these examples we supply additional information to the aes() function to define what information to display and how it is represented in the plot.
First we can recreate the plot we saw earlier.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height,Weight))
pcPlot+geom_point()
Now we can adjust the aes mapping by supplying an argument to the colour parameter in the aes function. (Note that ggplot2 accepts “color” or “colour” as parameter name)
This simple adjustment allows for identifaction of the separation between male and female measurements for height and weight.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height,y=Weight,colour=Sex))
pcPlot+geom_point()
Similarly the shape of points may be adjusted.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height,y=Weight,shape=Sex))
pcPlot+geom_point()
The aesthetic mappings may be set directly in the geom_points() function as previously when specifying red. This can allow the same ggplot object to be used by different aesethetic mappings and varying geoms
pcPlot <- ggplot(data=patients_clean)
pcPlot+geom_point(aes(x=Height,y=Weight,colour=Sex))
pcPlot+geom_point(aes(x=Height,y=Weight,colour=Smokes))
pcPlot+geom_point(aes(x=Height,y=Weight,colour=Smokes,shape=Sex))
pcPlot+geom_violin(aes(x=Sex,y=Height,fill=Smokes))
One very useful feature of ggplot is faceting. This allows you to produce plots subsets by variables in your data.
To facet our data into multiple plots we can use the facet_wrap or facet_grid function specifying the variable we split by.
The facet_grid() is well suited to splitting the data by two factors.
Here we can plot the data with the Smokes variable as rows and Sex variable as columns.
facet_grid(Rows~Columns)
pcPlot <- ggplot(data=patients_clean,aes(x=Height,y=Weight,colour=Sex))+geom_point()
pcPlot + facet_grid(Smokes~Sex)
To split by one factor we can apply the facet_grid() function ommiting the variable before the “~”" to facet along rows in plot.
facet_grid(~Columns)
pcPlot <- ggplot(data=patients_clean,aes(x=Height,y=Weight,colour=Sex))+geom_point()
pcPlot + facet_grid(~Sex)
To split along rows in plot the variable is place before the “~.”.
facet_grid(Rows~.)
pcPlot <- ggplot(data=patients_clean,aes(x=Height,y=Weight,colour=Sex))+geom_point()
pcPlot + facet_grid(Sex~.)
The geom_wrap offers a less grid based structure but is well suited to faceting data by one variable.
By one variable we follow as similar syntax to facet_grid()
pcPlot <- ggplot(data=patients_clean,aes(x=Height,y=Weight,colour=Sex))+geom_point()
pcPlot + facet_wrap(~Smokes)
For more complex faceting both facet_grid and facet_wrap can accept combinations of variables.
Using geom_wrap
pcPlot <- ggplot(data=patients_clean,aes(x=Height,y=Weight,colour=Sex))+geom_point()
pcPlot + facet_wrap(~Pet+Smokes+Sex)
Or in a nice grid format using facet_grid() and the Smokes variable against a combination of Gender and Pet.
pcPlot + facet_grid(Smokes~Sex+Pet)
Scales and their legends have so far been handled using ggplot2 defaults. ggplot2 offers functionality to have finer control over scales and legends using the scale methods.
Scale methods are divided into by functions by combinations of
the aesthetics they control
the type of data mapped to
scale_aesthetic_type
Try typing in scale_ then tab to autocomplete. This will provide some examples of the scale functions available in ggplot2.
Although different scale- functions accept some variety of paramters common arguments to scale functions include -
name - The axis or legend title
limits - Minimum and maximum of the scale
breaks - Label/tick positions along an axis
labels - Label names at each break
Both continous and discrete X/Y scales can be controlled in ggplot2 using the
scale_(x/y)_(continous/discrete)
In this example we control the continuous sale on the x-axis by providing a name, X-axis limits, the positions of breaks (ticks/labels) and the labels to place at breaks.
pcPlot +
geom_point() +
facet_grid(Smokes~Sex)+
scale_x_continuous(name="height ('cm')",
limits = c(100,200),
breaks=c(125,150,175),
labels=c("small","justright","tall"))
Similary control over discrete scales is shown below.
pcPlot <- ggplot(data=patients_clean,aes(x=Sex,y=Height))
pcPlot +
geom_violin(aes(x=Sex,y=Height)) +
scale_x_discrete(labels=c("Girls","Guys"))
Multiple X/Y scales can be combined to give full control of axis marks.
pcPlot <- ggplot(data=patients_clean,aes(x=Sex,y=Height,fill=Smokes))
pcPlot +
geom_violin(aes(x=Sex,y=Height)) +
scale_x_discrete(labels=c("Guys","Girls"))+
scale_y_continuous(breaks=c(160,180),labels=c("Petite","Tall"))
When using fill,colour,shape,size or alpha aesthetic mappings the scales are automatically selected for you and the appropriate legends created.
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,colour=Sex))
pcPlot + geom_point(size=4)
In the above example the discrete colours for the Sex variable was selected by default.
Manual control of discrete variables can be performed using scale_aes_Of_Interest_manual with the values parameter. Additionally an updated name for the legend is provided.
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,colour=Sex))
pcPlot + geom_point(size=4) +
scale_color_manual(values = c("Green","Purple"),
name="Gender")
Here we have specified the colours to be used (hence the manual) but when the number of levels to a variable are high this may be impractical and often we would like ggplot2 to choose colours from a scale of our choice.
The brewer set of scale functions allow the user to make use of a range of pallets available from colorbrewer.
BrBG, PiYG, PRGn, PuOr, RdBu, RdGy, RdYlBu, RdYlGn, Spectral
Accent, Dark2, Paired, Pastel1, Pastel2, Set1, Set2, Set3
Blues, BuGn, BuPu, GnBu, Greens, Greys, Oranges, OrRd, PuBu, PuBuGn, PuRd, Purples, RdPu, Reds, YlGn, YlGnBu, YlOrBr, YlOrRd
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,colour=Pet))
pcPlot + geom_point(size=4) +
scale_color_brewer(palette = "Set2")
For more details on palette sizes and styles visit the colorbrewer website.
So far we have looked a qualitative scales but ggplot2 offers much functionality for continuous scales such as for size, alpha (transparancy), colour and fill.
scale_alpha_continuous() - For Transparancy scale_size_continuous() - For control of size.
Both these functions accept the range of alpha/size to be used in plotting.
Below the range of alpha to be used in plot is limited to between 0.5 and 1
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,alpha=BMI))
pcPlot + geom_point(size=4) +
scale_alpha_continuous(range = c(0.5,1))
Below the range of sizes to be used in plot is limited to between 3 and 6
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,size=BMI))
pcPlot + geom_point(size=4) +
scale_size_continuous(range = c(3,6))
The limits of the scale can also be controlled.
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,size=BMI))
pcPlot + geom_point() + scale_size_continuous(range = c(3,6), limits = c(25,40))
## Warning: Removed 14 rows containing missing values (geom_point).
What points of scale to be displayed and the labels for scale can alos be controlled.
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,size=BMI))
pcPlot + geom_point() + scale_size_continuous(range = c(3,6), limits = c(25,40),breaks=c(25,35),labels=c("Good","Not So Good"))
## Warning: Removed 14 rows containing missing values (geom_point).
Control of colour/fill scales can be best achieved through the gradient subfunctions of scale.
scale_(colour/fill)gradient - 2 colour gradient (eg. low to high BMI) scale(colour/fill)_gradient2 - Diverging colour scale with a midpoint colour (e.g. Down, No Change, Up)
Both functions take a common set of arguments:-
An example using scale_colour_gradient below set the low and high end colours to White and Red respectively
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,colour=BMI))
pcPlot + geom_point(size=4,alpha=0.8) +
scale_colour_gradient(low = "White",high="Red")
Similarly we can use the scael_colour_gradient2 function which allows for the specification of a midpoint value and associated colour.
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,colour=BMI))
pcPlot + geom_point(size=4,alpha=0.8) +
scale_colour_gradient2(low = "Blue",mid="Black",high="Red",midpoint = median(patients_clean$BMI))
As with previous continous scales, limits and custom labels in scale legend can be added.
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,colour=BMI))
pcPlot + geom_point(size=4,alpha=0.8) + scale_colour_gradient2(low = "Blue",
mid="Black",
high="Red",
midpoint = median(patients_clean$BMI),
breaks=c(25,30),labels=c("Low","High"),
name="Body Mass Index")
Multiple scales may be combined to create high customisable plots and scales
pcPlot <- ggplot(data=patients_clean,
aes(x=Height,y=Weight,colour=BMI,shape=Sex))
pcPlot + geom_point(size=4,alpha=0.8)+ scale_shape_discrete(name="Gender") +scale_colour_gradient2(low = "Blue",mid="Black",high="Red",midpoint = median(patients_clean$BMI),
breaks=c(25,30),labels=c("Low","High"),
name="Body Mass Index")
In ggplot2 many of the statistical transformations are performed without any direct specification e.g. geom_histogram() will use stat_bin() function to generate bin counts to be used in plot.
An example of statistical methods which are very useful ggplot2 include the stat_smooth() and stat_summary() functions.
The stat_smooth() function can be used to fit a line to the data being displayed.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Weight,y=Height))
pcPlot+geom_point()+stat_smooth()
By default a “loess” smooth line is plotted by stat_smooth. Other methods available include lm, glm,gam,rlm.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Weight,y=Height))
pcPlot+geom_point()+stat_smooth(method="lm")
A useful feature of ggplot2 is that it uses previously defined grouping when performing smoothing.
If i add colour by Sex as an aesthetic mapping then two smooth lines are drawn, one for each sex.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Weight,y=Height,colour=Sex))
pcPlot+geom_point()+stat_smooth(method="lm")
This behaviour can be overridden by specifying an aes within the stat_smooth() function and setting inherit.aes to FALSE.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Weight,y=Height,colour=Sex))
pcPlot+geom_point()+stat_smooth(aes(x=Weight,y=Height),method="lm",inherit.aes = F)
Another useful method is stat_summary() which allows for the custom statistical function to be performed and the visualised.
The fun.y parameter allows the specification of a function to apply to the y variable for every value of x.
In this example we use it to plot the quantiles of the Female and Male Height data
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Sex,y=Height))+geom_jitter()
pcPlot+stat_summary(fun.y=quantile,geom="point",colour="purple",size=8)
Themes specify the details of data independent elements of the plot. This includes titles, background colour, text fonts etc.
The graphs created so far have all used the default themes ,theme_grey(), but ggplot2 allows for the specification of theme used.
Predefined themes can be applied to a ggplot2 object using a family of functions theme_style()
In the example below the minimal theme is applied to the scatter plot seen earlier.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Weight,y=Height))+geom_point()
pcPlot+theme_minimal()
Several predifined themes are available within ggplot2 including:
theme_bw
theme_classic
theme_dark
theme_gray
theme_light
theme_linedraw
theme_minimal
Packages such as ggthemes also contain many useful collections of predined theme_style functions.
As well as making use of predifened theme styles, ggplot2 allows for control over the attributes and elements within a plot through a collection of related functions and attributes.
theme() is the global function used to set the collections of elements’ attributes for the current plot.
Within the theme functions there are 3 general graphic elements which may be controlled:-
and 5 groups of related elements:-
When required these elements may be specified by the use of element functions including:
and additionally element_blank() to set an element to “blank”
To better demonstrate this, the below example sets the theme to have all rect, line and text elements as “blank”. We can see we get no axis, legend or labels.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Weight,y=Height))+geom_point()
pcPlot+theme(
rect = element_blank(),
line = element_blank(),
text = element_blank()
)
When defining a custom theme, it is useful to create a function to allow for the alteration of specific elements while maintaining the overall style.
In the example below , a function is defined ,theme_black, to create a black theme while allowing for the specification of text size and font. Here the full list of potential element attributes is listed.
theme_black <- function(base_size = 12, base_family = "Helvetica") {
theme(
line = element_line(colour = "black", size = 0.5, linetype = 1,
lineend = "butt"),
rect = element_rect(fill = "white", colour = "black", size = 0.5, linetype = 1),
text = element_text(family = base_family, face = "plain",
colour = "black", size = base_size,
hjust = 0.5, vjust = 0.5, angle = 0, lineheight = 0.9,
margin=margin(4),debug=F),
axis.text = element_text(size = rel(0.8), colour = "white",margin=margin(5,5,10,5,"pt")),
strip.text = element_text(size = rel(0.8), colour = "white"),
axis.line = element_blank(),
axis.text.x = element_text(vjust = 1),
axis.text.y = element_text(hjust = 1),
axis.ticks = element_line(colour = "white", size = 0.2),
axis.title = element_text(colour = "white"),
axis.title.x = element_text(vjust = 1),
axis.title.y = element_text(angle = 90),
axis.ticks.length = unit(0.3, "lines"),
legend.background = element_rect(colour = NA),
legend.margin = unit(0.2, "cm"),
legend.key = element_rect(fill = "black", colour = "white"),
legend.key.size = unit(1.2, "lines"),
legend.key.height = NULL,
legend.key.width = NULL,
legend.text = element_text(size = rel(0.8), colour = "white"),
legend.text.align = NULL,
legend.title = element_text(size = rel(0.8), face = "bold", hjust = 0, colour = "white"),
legend.title.align = NULL,
legend.position = "right",
legend.direction = "vertical",
legend.justification = "center",
legend.box = NULL,
panel.background = element_rect(fill = "black", colour = NA),
panel.border = element_rect(fill = NA, colour = "white"),
panel.grid.major = element_line(colour = "grey20", size = 0.2),
panel.grid.minor = element_line(colour = "grey5", size = 0.5),
panel.margin = unit(0.25, "lines"),
strip.background = element_rect(fill = "grey30", colour = "grey10"),
strip.text.x = element_text(),
strip.text.y = element_text(angle = -90),
plot.background = element_rect(colour = "black", fill = "black"),
plot.title = element_text(size = rel(1.2)),
plot.margin = unit(c(1, 1, 0.5, 0.5), "lines"),
complete = TRUE
)
}
Now the function has been defined it can be applied to the plot.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Weight,y=Height))+geom_point(colour="White")
pcPlot+theme_black(base_size = 15)
So far no plot titles have been specified. Plot titles can be specified using the labs functions.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Weight,y=Height))+geom_point()
pcPlot+labs(title="Weight vs Height",y="Height (cm)")
or specified using the ggtitle and xlab/ylab functions.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Height,y=Weight))+geom_point()
pcPlot+ggtitle("Weight vs Height")+ylab("Height (cm)")
Plots produced by ggplot can be saved from the interactive viewer as with standard plots.
The ggsave() function allows for additional arguments to be specified including the type, resolution and size of plot.
By default ggsave() will used the size of your current graphics window when saving plots so it may be important to specify width and height arguments desired.
pcPlot <- ggplot(data=patients_clean,
mapping=aes(x=Weight,y=Height))+geom_point()
ggsave(pcPlot,filename = "anExampleplot.png",width = 15,height = 15,units = "cm")
Scale added automatically colour/color alpha size
Show turning discrete from continous with factor type shape
Discrete Continuous Manual
stat_smooth stat_summary
Blank_theme theme_get and theme_set ggthemes Themes to adjust labels. Creating your own theme XKCD theme
pcPlot <- ggplot(data=patients_clean,aes(y=Weight,x=Height,colour=Sex,size=BMI,shape=Pet))+geom_point() pcPlot